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NicolasHug
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Feb 15, 2023
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NicolasHug
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Thank you Philip, if it fixes the original failure then let's get this in
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pmeier
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Feb 15, 2023
| def _transform( | ||
| self, inpt: Union[datapoints.ImageType, datapoints.VideoType], params: Dict[str, Any] | ||
| ) -> Union[List[datapoints.ImageTypeJIT], List[datapoints.VideoTypeJIT]]: | ||
| ) -> Tuple[ |
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Yes, all of this is ugly and verbose AF. We can also try to keep the List annotation (which we should have gone for in the first place).
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| flipped = five_crop_image_pil(image, size) | ||
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| return non_flipped + flipped |
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This is the actual functional fix. v1 returned a tuple and so should v2.
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Test failures are unrelated. |
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Reviewed By: vmoens Differential Revision: D44416586 fbshipit-source-id: e506d56435133a7761f59a3ac9d0208f1fab4884
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Towards #7159. Although we annotated as
List[torch.Tensor]vision/torchvision/transforms/functional.py
Line 830 in 0e0a5dc
the addition of two tuples
vision/torchvision/transforms/functional.py
Lines 870 to 871 in 0e0a5dc
vision/torchvision/transforms/functional.py
Line 785 in 0e0a5dc
will also be a tuple.
Our docstrings also say as much:
vision/torchvision/transforms/functional.py
Lines 848 to 849 in 0e0a5dc
vision/torchvision/transforms/transforms.py
Lines 1073 to 1074 in c5e9a10
It seems the automagic handling described in #7252 also kicks in for return types or otherwise the v1 script tests should have never passed.
cc @vfdev-5 @bjuncek